The experience of unresolved curiosity is inherently frustrating, as well-demonstrated by a novel that ends with a cliff-hanger but has no sequel in sight. Curious humans modify their behaviour to alleviate the feeling of unresolved curiosity.[1]} provide an overview of the lengths people will go to to satisfy their curiosity, including paying for non-instrumental information (information that provides no benefit in terms of traditional extrinsic rewards, like money or food) or exposing themselves to pain or risk. Despite the aversive quality or discomfort associated with curiosity,The idea that being in a condition of curiosity is uncomfortable has sparked some debate. [2]} has argued that the idea of curiosity as aversive is a longstanding assumption with little supporting evidence popularized by [3]}'s seminal work ([3]}). The difficulty in disentangling evidence of an aversive quality to curiosity from other possible motivating factors still stands in more recent work [5]}. Indeed, recent accounts of how emotions are constructed in biological brains and bodies suggests that the experience of curiosity may vary by culture [6]}, and individual differences implicated in interpretation the experience of curiosity may account for some of the controversy. In the computational part of this work (Section ), we take inspiration from the aversive quality of curiosity, but our computational analogue of aversive quality is not needed for our computational learner to demonstrate recognizably curious behaviour (Section REF ). humans voluntarily expose themselves to curiosity, choosing to pick up mystery novels and puzzles because they will pique curiosity [3]}. We aim to capture this tendency with our third property, voluntary exposure.
